The first assumption indicates that the potential outcomes of subjects in each treatment group are independent of the treatment assigned after conditioning on the selected covariates. It relies on all confounders of the effect of treatment on outcome being measured and accounted for [1]}. The second assumption indicates that, for every participant in the index study, the probability of being assigned to either treatment is positive, conditional on the covariates selected to ensure exchangeability [2]}. This requires overlap between the joint covariate distributions of the subjects under treatment \(A\)  and under treatment \(C\) . This assumption is threatened if there are few or no individuals from either treatment group in certain covariate subgroups/strata.
